There are many open source projects you rely on. Watch past webinars where developers and leaders of open-source projects talk about where the project is heading.

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Episode #29: Panel

Featuring Developers: Julia Signell and Philipp Rudiger

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Episode #29

Air Date 27 September 2019

@12 PM Eastern

We will be joined by Julia Signell and Philipp Rudiger developers on the Panel project, who will tell us about the future of Panel. Panel is a new open-source Python library that lets you create custom interactive web apps and dashboards by connecting user-defined widgets to plots, images, tables, or text. It is the culmination of a multi-year effort to connect data scientists with tools for deploying the output of their analysis and models with internal or external consumers of the analysis without having to learn completely different technology stacks or getting into the weeds of web development.

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Episode #27: Open Teams

Featuring Open Teams Representatives: Eunice Chendjou and David Charboneau

Episode #27

Air Date 23 August 2019

@12 PM Eastern

We will be joined by Eunice Chendjou and David Charboneau, lead community managers for OpenTeams, who will tell us about the future of OpenTeams. OpenTeams helps connect open source communities to organizations using open source and helps open source contributors get credit for their contributions to open source projects.

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Past Webinars

Episode #26: Vega

Featuring Vega Developers: Dominik Moritz and Kanit Wongsuphasawat

Episode #26

Air Date 9 August 2019

@12 PM Eastern

We will be joined by Dominik Moritz andKanit Wongsuphasawat, programmers on the Vega project, who will tell us about the future of Vega. Vega is a visualization grammar, a declarative language for creating, saving, and sharing interactive visualization designs. With Vega, you can describe the visual appearance and interactive behavior of a visualization in a JSON format, and generate web-based views using Canvas or SVG.

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Episode #25: Binder

Featuring Binder Developers: Chris Holdgraf &

Min Ragan-Kelley

Episode #25

Air Date 26 July 2019

@12 PM Eastern

We will be joined by Chris Holdgraf, who will tell us about the future of Binder. Binder is an open-source web application for managing digital repositories. It is particularly adept at supporting the care, management, and preservation of complex digital collections such as time-based media and born- digital artworks.

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Episode #24: nteract

Featuring nteract Developer: Safia Abdalla

Episode #24

Air Date 12 July 2019

@12 PM Eastern

We will be joined by Safia Abdalla, who will demo and tell us about the future of nteract. The mission of nteract is to create fantastic interactive computing experiences that allow people to collaborate with ease. They emphasize simplicity and composability as core design principles to provide users ideal building blocks for their unique data applications.

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Episode #23: conda-forge

Featuring conda-forge Developers: Marius van Niekerk and John Kirkham

Episode #23

Air Date 28 June 2019

@12 PM Eastern

We will be joined by Marius van Niekerk and John Kirkham, who will tell us about the future of conda-forge. Conda-forge is a github organization containing repositories of conda recipes. The built distributions are uploaded to anaconda.org/conda-forge and can be installed with conda.

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Episode #22: Scikit-learn​

Featuring Scikit-learn Developers: Andreas Mueller

Episode #22

Air Date 31 May 2019

@12 PM Eastern

We will be joined by Andreas Mueller, who will tell us about the future of Scikit-learn. Scikit-learn is a simple and efficient open source tool for data mining and data analysis. It is the standard tool for Machine Learning in Python and has been made accessible to everybody. It is reusable in various contexts and is built on NumPy, SciPy, and matplotlib.

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Episode #21: xtensor/xframe

Featuring xtensor/xframe Developer: Sylvain Corlay

Episode #21

Air Date 17 May 2019

@12 PM Eastern

We will be joined by Sylvain Corlay, who will demo and tell us about the future of xtensor and xframe. Xtensor is a C++ library meant for numerical analysis with multi-dimensional array expressions, and xframe is a data-frame for C++, based on xtensor and xtl. While xtensor is an established tool, xframe is an early developer preview, and is not suitable for general usage yet. We are excited to see the direction that both of these projects are headed.

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Episode #20: Uarray​

Featuring Uarray Developer: Saul Shanabrook

Episode #20

Air Date 3 May 2019

@12 PM Eastern

We will be joined again by Hameer Abbasi and Saul Shanabrook, who will tell us about the future of Uarray. The goal of uarray is to allow backends but preserve a NumPy-like interface for those backends. For example, it’s possible the same API to use NumPy, Dask and other array-like objects with the same API without changing your code.

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Episode #19: Pyodide

Featuring Pyodide Developer: Michael Droettboom

Episode #19

Air Date 19 April 2019

@12 PM Eastern

We will be joined by Michael Droettboom, who will tell us about the future of Pyodide. Pyodide allows you to build a data science notebook based on web technologies. Sharing a notebook is as simple as passing around a single HTML file, since there's no server side to worry about. Though similar to Jupyter Notebooks, it exists in a different design trade off space and is designed around sharing and collaboration.

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Episode #18: PyMC3/4

Featuring PyMC3 Developer: Christopher Fonnesbeck

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Episode #18

Air Date 5 April 2019

@12 PM Eastern

We will be joined by Christopher Fonnesbeck, who will tell us about the future of PyMC3/4. PyMC3/4 allows you to write down models using an intuitive syntax to describe a data generating process. Furthermore, you can fit your model using gradient-based MCMC algorithms like NUTS, using ADVI for fast approximate inference — including minibatch-ADVI for scaling to large datasets — or using Gaussian processes to build Bayesian nonparametric models.

Episode #17: Tensorflow

The webinar will feature

Paige Bailey, who will tell us about the future of Tensorflow. TensorFlow is an open source machine learning framework for everyone. It works with Python and provides high-performance numerical computation that makes machine learning faster and easier.

Episode #16: Chainer

The webinar will feature

Crissman Loomis, who will tell us about the future of Chainer. Chainer is a powerful, flexible, and intuitive deep learning framework. Chainer supports CUDA computation, and requires only a few lines of code to leverage GPU and runs on multiple GPUs with little effort.

Episode #15: Numba

The webinar will feature

Stanley Seibert, who will tell us about the future of Numba. Numba translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN.

Episode #14: ITK

The webinar will feature

Matt McCormick and Beatriz Paniagua, who will tell us about the future of ITK. ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT, MRI or ultrasound scanners. Registration is the task of aligning or developing correspondences between data.

Episode #13: Jupyter Ecosystem

The webinar will feature

Matthias Bussonnier and Carol Willing, who will tell us about the future of the Jupyter Ecosystem. The Jupyter Ecosystem is a collection of tools which are oriented towards improving the lives of data analysts. Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages.

Episode #12: PySpark

We will be joined by Holden Karau, who will tell us about the future of PySpark. PySpark is the Python API for Spark, it exposes the Spark programming model to Python helping data scientists interface with Resilient Distributed Datasets in apache spark. python.Py4J is a popular library integrated within PySpark that lets python interface dynamically with JVM objects (RDD's). Apache Spark comes with an interactive shell for python as it does for Scala.

Episode #11: Dask

The webinar will feature

Jim Crist and Tom Augspurger, discussing Dask. Dask ML has two goals: Scale machine learning to more computation; and scale machine learning to big data. Dask’s architecture can scale to many machines using parallelism for optimization.

Episode #10: PyData/Sparse

The webinar will feature

Hameer Abbasi, discussing PyData/Sparse. PyData/Sparse implements sparse arrays of arbitrary dimension on top of NumPy and scipy.sparse. He will discuss the roadmap for future implementations and the eventual goals of this project.

Episode #9: Datashader

Featuring Datashader Developer: Jim Bednar

Episode #09

Air Date 14 December 2018

@12 PM Eastern

The webinar will feature

Jim Bednar, discussing Datashader is a graphics pipeline system for creating meaningful representations of large datasets quickly and flexibly. He will discuss the roadmap for future implementations and the eventual goals of this project.

Episode #7: GeoViews

Featuring GeoViews Developer: Philipp Rudiger

Episode #07

Air Date 16 November 2018

@12 PM Eastern

The webinar will feature Philipp Rudiger, discussing GeoViews is a Python library that makes it easy to explore and visualize geographical, meteorological, and oceanographic datasets,such as those used in weather, climate, and remote sensing research. He will discuss the roadmap for future implementations and the eventual goals of this project.

Episode #6: Intake

Featuring Intake Developer: Martin Durant

Episode #06

Air Date 26 October 2018

@12 PM Eastern

The webinar will feature Martin Durant, discussing Intake which is a lightweight set of tools for loading and sharing data in data science projects.
He will discuss the roadmap for future implementations and the eventual goals of this project.

Episode #5: CuPy

Featuring CuPy Developer: Crissman Loomis

Episode #05

Air Date 18 October 2018

@9 PM Eastern

We will be joined by Crissman Loomis, who will tell us about the future of CuPy. CuPy is an open-source matrix library accelerated with NVIDIA CUDA. It also uses CUDA-related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT, and NCCL to make full use of the GPU architecture.

Episode #4: PyViz

Featuring PyViz Developer: Jim Bednar

Episode #04

Air Date 12 October 2018

@12 PM Eastern

The webinar will feature Jim Bednar, discussing PyViz which is an initiative to coordinate scientific Python libraries so that they can work well together and jointly solve a wide range of problems in data science, visualization, and analysis. He will discuss the roadmap for future implementations and the eventual goals of this project.

Episode #3: SymPy

Featuring SymPy Developer: Aaron Meurer

Episode #03

Air Date 28 September 2018

@12 PM Eastern

The webinar will feature

Aaron Meurer, discussing SymPy which provides a library for symbolic mathematics. He will discuss the roadmap to becoming a full-featured computer algebra system while keeping the code as simple as possible.

Episode #1: Spyder

The webinar features Carlos Cordoba with Spyder, a popular IDE. Carlos walks through current and future developments. The Scientific PYthon Development EnviRonment helps you streamline your data crunching and will be familiar to anyone who has used IDEs such as MATLAB or RStudio.

Episode #0: Bokeh

Featuring Lead Bokeh develoer Bryan Van de Ven

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Episode #00

Air Date 20 July 2018

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This webinar features Bokeh which is a web-based and interactive visualization library for Python. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.